如何在Python中使用模拟退火算法进行查找

import random
import math

# 定义初始字符串
str_target = "pidancode.com"

# 计算字符串的能量
def get_energy(str_input):
    diff = 0
    for i in range(len(str_input)):
        diff += abs(ord(str_input[i]) - ord(str_target[i]))
    return diff

# 定义模拟退火算法
def simulated_annealing(str_input, temperature=1.0, cooling_rate=0.99, min_temperature=0.00001):
    # 初始化字符串和能量
    str_current = str_input
    energy_current = get_energy(str_current)

    # 初始化最优字符串和能量
    str_best = str_current
    energy_best = energy_current

    # 循环迭代
    while temperature > min_temperature:
        # 随机生成新字符串
        pos = random.randint(0, len(str_current)-1)
        str_new = str_current[:pos] + chr(ord(str_current[pos]) + random.randint(-1, 1)) + str_current[pos+1:]

        # 计算新字符串的能量
        energy_new = get_energy(str_new)

        # 计算能量差
        diff = energy_new - energy_current

        # 判断是否接受新字符串
        if diff < 0 or math.exp(-(diff/temperature)) > random.random():
            str_current = str_new
            energy_current = energy_new

        # 更新最优字符串
        if energy_current < energy_best:
            str_best = str_current
            energy_best = energy_current

        # 降低温度
        temperature *= cooling_rate

    return str_best

# 测试示例
str_input = "皮蛋编程"
print("原始字符串为：", str_input)
print("目标字符串为：", str_target)
print("初始能量为：", get_energy(str_input))

str_optimal = simulated_annealing(str_input)
energy_optimal = get_energy(str_optimal)

print("优化后的字符串为：", str_optimal)
print("优化后的能量为：", energy_optimal)